get_CLE_comparison

list object that contains all error values for all trained calibration models. For the specific format, see the calling function <code><a rd-options="" href="/link/visualize_calibratR?package=CalibratR&version=0.1.2" data-mini-rdoc="CalibratR::visualize_calibratR">visualize_calibratR</a></code>.

list_models

visualises how class 1 and class 0 classification error (CLE) differs in each trained calibration model.
Comparing class-specific CLE helps to choose a calibration model for applications were classification error is cost-sensitive for one class.
See <code><a rd-options="" href="/link/get_CLE_class?package=CalibratR&version=0.1.2" data-mini-rdoc="CalibratR::get_CLE_class">get_CLE_class</a></code> for details on the implementation.

Transforms your uncalibrated Machine Learning scores to well-calibrated prediction estimates that can be interpreted as probability estimates. The implemented BBQ (Bayes Binning in Quantiles) model is taken from Naeini (2015, ISBN:0-262-51129-0). Please cite this paper: Schwarz J and Heider D, Bioinformatics 2019, 35(14):2458-2465.

Dominik Heider

CalibratR

Mapping ML Scores to Calibrated Predictions

get_CLE_comparison function

list object that contains all error values for all trained calibration models. For the specific format, see the calling function <code><a rd-options='' href='visualize_calibratR'>visualize_calibratR</a></code>.

visualises how class 1 and class 0 classification error (CLE) differs in each trained calibration model.
Comparing class-specific CLE helps to choose a calibration model for applications were classification error is cost-sensitive for one class.
See <code><a rd-options='' href='get_CLE_class'>get_CLE_class</a></code> for details on the implementation.

get_CLE_comparison: get_CLE_comparison

Description

Usage

Arguments

Value